The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery
Drug discovery and development are aimed at identifying new chemical molecular entities (NCEs) with desirable pharmacokinetic profiles for high therapeutic efficacy. The plasma concentrations of NCEs are a biomarker of their efficacy and are governed by pharmacokinetic processes such as absorption,...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Pharmaceutics |
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| Online Access: | https://www.mdpi.com/1999-4923/15/11/2619 |
| _version_ | 1797458014557437952 |
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| author | Hiroshi Komura Reiko Watanabe Kenji Mizuguchi |
| author_facet | Hiroshi Komura Reiko Watanabe Kenji Mizuguchi |
| author_sort | Hiroshi Komura |
| collection | DOAJ |
| description | Drug discovery and development are aimed at identifying new chemical molecular entities (NCEs) with desirable pharmacokinetic profiles for high therapeutic efficacy. The plasma concentrations of NCEs are a biomarker of their efficacy and are governed by pharmacokinetic processes such as absorption, distribution, metabolism, and excretion (ADME). Poor ADME properties of NCEs are a major cause of attrition in drug development. ADME screening is used to identify and optimize lead compounds in the drug discovery process. Computational models predicting ADME properties have been developed with evolving model-building technologies from a simplified relationship between ADME endpoints and physicochemical properties to machine learning, including support vector machines, random forests, and convolution neural networks. Recently, in the field of in silico ADME research, there has been a shift toward evaluating the in vivo parameters or plasma concentrations of NCEs instead of using predictive results to guide chemical structure design. Another research hotspot is the establishment of a computational prediction platform to strengthen academic drug discovery. Bioinformatics projects have produced a series of in silico ADME models using free software and open-access databases. In this review, we introduce prediction models for various ADME parameters and discuss the currently available academic drug discovery platforms. |
| first_indexed | 2024-03-09T16:31:00Z |
| format | Article |
| id | doaj.art-052c0a40730d4ffdb04200eca909039f |
| institution | Directory Open Access Journal |
| issn | 1999-4923 |
| language | English |
| last_indexed | 2024-03-09T16:31:00Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pharmaceutics |
| spelling | doaj.art-052c0a40730d4ffdb04200eca909039f2023-11-24T15:01:09ZengMDPI AGPharmaceutics1999-49232023-11-011511261910.3390/pharmaceutics15112619The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug DiscoveryHiroshi Komura0Reiko Watanabe1Kenji Mizuguchi2University Research Administration Center, Osaka Metropolitan University, 1-2-7 Asahimachi, Abeno-ku, Osaka 545-0051, Osaka, JapanInstitute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita 565-0871, Osaka, JapanInstitute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita 565-0871, Osaka, JapanDrug discovery and development are aimed at identifying new chemical molecular entities (NCEs) with desirable pharmacokinetic profiles for high therapeutic efficacy. The plasma concentrations of NCEs are a biomarker of their efficacy and are governed by pharmacokinetic processes such as absorption, distribution, metabolism, and excretion (ADME). Poor ADME properties of NCEs are a major cause of attrition in drug development. ADME screening is used to identify and optimize lead compounds in the drug discovery process. Computational models predicting ADME properties have been developed with evolving model-building technologies from a simplified relationship between ADME endpoints and physicochemical properties to machine learning, including support vector machines, random forests, and convolution neural networks. Recently, in the field of in silico ADME research, there has been a shift toward evaluating the in vivo parameters or plasma concentrations of NCEs instead of using predictive results to guide chemical structure design. Another research hotspot is the establishment of a computational prediction platform to strengthen academic drug discovery. Bioinformatics projects have produced a series of in silico ADME models using free software and open-access databases. In this review, we introduce prediction models for various ADME parameters and discuss the currently available academic drug discovery platforms.https://www.mdpi.com/1999-4923/15/11/2619in silico modelADMEartificial intelligencemachine learningpredictionacademic drug discovery |
| spellingShingle | Hiroshi Komura Reiko Watanabe Kenji Mizuguchi The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery Pharmaceutics in silico model ADME artificial intelligence machine learning prediction academic drug discovery |
| title | The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery |
| title_full | The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery |
| title_fullStr | The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery |
| title_full_unstemmed | The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery |
| title_short | The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery |
| title_sort | trends and future prospective of in silico models from the viewpoint of adme evaluation in drug discovery |
| topic | in silico model ADME artificial intelligence machine learning prediction academic drug discovery |
| url | https://www.mdpi.com/1999-4923/15/11/2619 |
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